The present invention relates generally to analyzing musical compositions represented in audio files/sources and more particularly to predicting and/or determining musical key information about the musical composition.
The capacity to accurately determine musical key information from a musical composition represented, for example, in a digital audio file has myriad applications. For instance, DJs and musicians often need accurate musical key information for audio sampling, remixing, or other DJ-related purposes. Specifically, musical key information can be used to create audio mash-ups, compose new songs, or overlay elements of one song with another song without experiencing a harmonic key clash. Although the need for musical key information is apparent, the method to obtain such information is not. Frequently, documentation concerning the musical composition is not available, e.g. sheet music, thereby frustrating any efforts directed toward discovering musical key information about the composition.
Even without the necessary documentation, musical key information about a composition can be determined by an artisan with a “trained” ear. Simply by listening to a musical composition, the artisan can proffer a reasonably accurate conclusion as to musical key information of the composition-in-question. Unfortunately, many are without such a skill set.
It is also known to use computer software to predict musical key information about a musical composition represented in an audio file. Representative software packages include Rapid Evolution available through Mixshare and MixMeister Studio marketed by MixMeister Technology, L.L.C. These software products allow an audio file or other source containing a musical composition to be analyzed for musical key information, although with varying degrees of success and utility.
Consider, for exemplary purposes, the following sequence illustrating one approach to extracting/predicting musical key information from a musical composition. Initially, the musical composition is decomposed into its constituent musical note components. The collection of constituent musical notes is then compared to a database of musical key templates—often twenty four templates, one for each musical key. Each template in the database describes the notes most commonly associated with a specific key. To predict musical key information, the software selects the template, i.e. musical key, with the highest correlation to the collection of constituent musical notes from the subject audio file. Moreover, the software may also provide correlation or probability information describing the relationship between the collection of constituent musical notes and each of the templates.
Unfortunately, the database of templates typically employed in these types of software applications is hampered by the style of compositions used to build the templates (styles or genres of music different from that used to generate the templates may distort the results) and the limited number of templates available, such as only twenty-four.
Thus, what is needed a musical key detection system that can readily accommodate different musical styles, have a database containing as many templates as desired, and provide additional metrics from which to more accurately predict musical key information from musical composition represented by digital audio signals.